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Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs).
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Tom B Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al · 2017
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Cyclical learning rates for training neural networks
Leslie N Smith · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
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Mitigating adversarial effects through randomization
Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Mma training: Direct input space margin maximization through adversarial training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Dpatch: An adversarial patch attack on object detectors
Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Li, and Yiran Chen · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
Cited alongside, same era.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2019
Cited alongside, same era.
Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 2019
Cited alongside, same era.
Intriguing properties of adversarial training at scale
Cihang Xie and Alan Yuille · 2019
Cited alongside, same era.
You only propagate once: Accelerating adversarial training via maximal principle
Cihang Xie, Mingxing Tan, Boqing Gong, Alan Yuille, and Quoc V Le · 2020
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Xcit: Cross-covariance image transformers
Alaaeldin Ali, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, et al · 2021
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Are transformers more robust than cnns?
Yutong Bai, Jieru Mei, Alan L Yuille, and Cihang Xie · 2021
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Improving adversarial robustness via channel-wise activation suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang · 2021
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Understanding robustness of transformers for image classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner, Daliang Li, Thomas Unterthiner, and Andreas Veit · 2021
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Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, and Bin Dong · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
Cited alongside, same era.
Understanding and improving fast adversarial training
Maksym Andriushchenko and Nicolas Flammarion · 2020
Cited alongside, same era.
Robustbench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, and Matthias Hein · 2020
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Cited alongside, same era.
Adversarial distributional training for robust deep learning
Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, and Hang Su · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
On adversarial robustness: A neural architecture search perspective
Chaitanya Devaguptapu, Devansh Agarwal, Gaurav Mittal, Pulkit Gopalani, and Vineeth N Balasubramanian · 2021
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Dsrna: Differentiable search of robust neural architectures
Ramtin Hosseini, Xingyi Yang, and Pengtao Xie · 2021
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Exploring architectural ingredients of adversarially robust deep neural networks
Hanxun Huang, Yisen Wang, Sarah Erfani, Quanquan Gu, James Bailey, and Xingjun Ma · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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On the adversarial robustness of vision transformers
Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, and Cho-Jui Hsieh · 2021
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Robustart: Benchmarking robustness on architecture design and training techniques
Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, et al · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2021
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Do wider neural networks really help adversarial robustness?
Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, and Quanquan Gu · 2021
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Parameterizing activation functions for adversarial robustness
Sihui Dai, Saeed Mahloujifar, and Prateek Mittal · 2022
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Skelevision: Towards adversarial resiliency of person tracking with multi-task learning
Nilaksh Das, Sheng-Yun Peng, and Duen Horng Chau · 2022
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A light recipe to train robust vision transformers
Edoardo Debenedetti, Vikash Sehwag, and Prateek Mittal · 2022
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Subspace adversarial training
Tao Li, Yingwen Wu, Sizhe Chen, Kun Fang, and Xiaolin Huang · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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When adversarial training meets vision transformers: Recipes from training to architecture
Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo, and Yisen Wang · 2022
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Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar · 2022
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